Earlier this week, I found myself answering a question from a new colleague at Finning International that relates both to the research I do in the iSchool at the University of British Columbia, as well as the analytics, engineering & technology work that I lead at Finning. The questions were simple: 1) What is artificial intelligence? As I sat to reflect last evening, it dawned on me that taking time to craft a clear answer to these questions might be extremely beneficial for many. Analytics, data science, and predictive intelligence are hot topics in many communities and business areas. And yet, despite this interest, few folks I have talked to have a clear understanding of the history of the discipline; one, that frames much of the work currently going on within the space.

This paper studies two issues concerning relevance in structured argumentation in the context of the ASPIC framework, arising from the combined use of strict and defeasible inference rules. One issue arises if the strict inference rules correspond to classical logic. A longstanding problem is how the trivialising effect of the classical Ex Falso principle can be avoided while satisfying consistency and closure postulates. In this paper, this problem is solved by disallowing chaining of strict rules, resulting in a variant of the ASPIC framework called ASPIC*, and then disallowing the application of strict rules to inconsistent sets of formulas. Another issue is minimality of arguments.

Plenty of media attention has been devoted to robots replacing lawyers. Conversely, some industry players claim that artificial intelligence (AI) is simply a buzzword used to sell software to law firms. "Are many AI-badged products just rule-based decision-making tools?" asks Alex Smith, platform innovation lead at LexisNexis UK. "What counts as AI?" AI in business has moved beyond process automation to include natural language processing and machine-learning, whereby computers are trained to interpret information and adjust their processes to user feedback. Rather than searching for keywords or strings of words, the software reads and understands information, so its findings and recommendations are based on contextual elements. Gerard Frith, chief executive of AI consultancy Matter, explains how AI adds value by modelling and reapplying expert knowledge in a fast, scalable way.

NFL coaching great Buddy Ryan, who passed away Tuesday at the age of 82, didn't have difficulty finding the spotlight wherever he went. From the New York Jets when they became the first AFL team to win a Super Bowl in 1968, to the Super Bears of 1985 to his last stop as the head man of the Arizona Cardinals in 1994-95, Ryan had a common theme -- "You got a winner in town." One of the best fits for that boisterous personality was his stop as head coach of the Philadelphia Eagles from 1986 to 1990. He was brash, but his teams backed up his words. He made fun of the rich owner, Norman Braman -- "that guy in France" -- who spent a lot of time overseas, and connected with the blue-collar town.

The number of sophisticated cognitive technologies that might be capable of cutting into the need for human labor is expanding rapidly. But linking these offerings to an organization's business needs requires a deep understanding of their capabilities. If popular culture is an accurate gauge of what's on the public's mind, it seems everyone has suddenly awakened to the threat of smart machines. Several recent films have featured robots with scary abilities to outthink and manipulate humans. In the economics literature, too, there has been a surge of concern about the potential for soaring unemployment as software becomes increasingly capable of decision making. Yet managers we talk to don't expect to see machines displacing knowledge workers anytime soon -- they expect computing technology to augment rather than replace the work of humans.

And with the social media giants battling for more users, Twitter has unleashed a new option that could help them stay in the game. Members can now create videos up to 140 seconds long and a small test group on Vine has also been granted this ability, allowing them to turn'the six second Vine into a trailer for a bigger story'. Twitter has unleashed a new feature that could help them stay in the game. The firm announced members can now create videos up to 140 seconds long and a small group on Vine also has this option, allowing them to turn'the six second Vine into a trailer for a bigger story' To start making longer videos, users simply tap on a video Tweet or Vine on their own timeline and will be redirected to a'new, full-screen viewing experience'. This new screen also gives you video editing tools, which allow users to trim down the clips to customize the beginning and end of the video.

Artificial Intelligence is a technology that uses human-like learning to perform tasks. The idea of Artificial Intelligence or AI is nothing new. As a concept it has been in our literature and art for centuries. But these ideas had no foundation other than as philosophies of nature and science fiction. Connectionist paradigms of Artificial Intelligence have a somewhat more flexible approach than their rule-based cousins, but both have applications in modern technologies. Machine learning is based on artificial neural networks, which are a much-simplified version of how our brains work.

Dan Schutzer (photo left), a senior technology consultant at the Financial Services Roundtable's BITS technology division defined artificial intelligence as'the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.' According to Schutzer, efforts in the 1990s to build artificial intelligence-like systems for use in financial services has resulted in'disillusionment as realization set in that these systems were harder and more costly to build and maintain than first anticipated.' Fast forward to 2015 when advances in high performance computing, algorithmic theory and cloud computing are bringing us closer to true AI capabilities for commercial use by the financial industry. Patrick Tucker, author of The Naked Future: What Happens In a World That Anticipates Your Every Move?, wrote that "When the cost of collecting information on virtually every interaction falls to zero, the insights that we gain from our activity, in the context of the activity of others, will fundamentally change the way we relate to one another, to institutions, and with the future itself." "One of the first things to note about AI is its ability to process enormous amounts of data very quickly and far more data than it's ever been processed in the past by humans or computer programs. That is going to enable banks to improve the services they provide to customers, including better, more targeted advice," said Astrid Raetze (photo right), a partner at Baker & McKenzie.

Structure Of Intelligent Agents 12. Types of agent program 13. Goal based Agents 14. Utility-based agents 15.

This paper studies two issues concerning relevance in structured argumentation in the context of the ASPIC+ framework, arising from the combined use of strict and defeasible inference rules. One issue arises if the strict inference rules correspond to classical logic. A longstanding problem is how the trivialising effect of the classical Ex Falso principle can be avoided while satisfying consistency and closure postulates. In this paper, this problem is solved by disallowing chaining of strict rules, resulting in a variant of the ASPIC+ framework called ASPIC*, and then disallowing the application of strict rules to inconsistent sets of formulas. Thus in effect Rescher & Manor's paraconsistent notion of weak consequence is embedded in ASPIC*. Another issue is minimality of arguments. If arguments can apply defeasible inference rules, then they cannot be required to have subset-minimal premises, since defeasible rules based on more information may well make an argument stronger. In this paper instead minimality is required of applications of strict rules throughout an argument. It is shown that under some plausible assumptions this does not affect the set of conclusions. In addition, circular arguments are in the new ASPIC* framework excluded in a way that satisfies closure and consistency postulates and that generates finitary argumentation frameworks if the knowledge base and set of defeasible rules are finite. For the latter result the exclusion of chaining of strict rules is essential. Finally, the combined results of this paper are shown to be a proper extension of classical-logic argumentation with preferences and defeasible rules.